Ming-Lang Tseng, Tat-Dat Bui, Ming K. Lim, Feng Ming Tsai and Raymond R. Tan
Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This…
Abstract
Purpose
Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.
Design/methodology/approach
A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.
Findings
The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.
Originality/value
This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.
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Yan Li, Ming K. Lim and Ming-Lang Tseng
This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of…
Abstract
Purpose
This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions.
Design/methodology/approach
This study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case.
Findings
The results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning.
Research limitations/implications
There are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions.
Originality/value
Prior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.
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Ming Lang Tseng, Viqi Ardaniah, Tat-Dat Bui, Jiun-Wei Tseng (Aaron), Ming K. Lim and Mohd Helmi Ali
Sustainable waste management (SWM) leads to human safety by eliminating dangerous substances, increasing cost efficiency and reducing environmental impacts. Integrating social…
Abstract
Purpose
Sustainable waste management (SWM) leads to human safety by eliminating dangerous substances, increasing cost efficiency and reducing environmental impacts. Integrating social, economic and environmental factors is the standard for successfully implementing SWM. However, prior studies have not incorporated the triple bottom line with technological performance and occupational safety in establishing SWM. To drive sustainability in waste management, this study aims to provide a set of SWM attributes and identify a causality model based on the interrelationships among the attributes.
Design/methodology/approach
This study used the Delphi method to list the relevant attributes and the decision-making trial and evaluation laboratory (DEMATEL) involving 18 experts from the medical and health-care industry to present the interrelationships indicating the group of cause–effect attributes of SWM.
Findings
The study selected 5 aspects and 20 criteria as the relevant attributes of SWM. The cause group consists of environmental impacts and occupational safety, with positive values of 27.031 and 24.499, respectively. The effect group includes technological performance, economic performance and social performance. In particular, the challenges and practices of technological performance are linked to environmental impacts and occupational safety.
Practical implications
The top four criteria for industrial improvement are green practices, government policy and rules, the awareness of workers and waste separation and collection. These results present deeper insights into theoretical and managerial implications.
Originality/value
This study contributes to addressing the challenges and practices of SWM in technological performance leading to environmental impacts and occupational safety. Studies on the technological performance aspect in the causality relationships between environmental impacts and occupational safety are lacking. This study describes SWM using qualitative information and quantitative data.
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Ming K. Lim, Yan Li, Chao Wang and Ming-Lang Tseng
The transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore…
Abstract
Purpose
The transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face.
Design/methodology/approach
This research proposes a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study.
Findings
The prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R2). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature.
Research limitations/implications
The case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM.
Originality/value
In prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.
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Ming K. Lim, Jianxin Wang, Chao Wang and Ming-Lang Tseng
Increasing academic communities and practitioners begin to explore a novel method to reduce environmental pollution and realize green logistics delivery. Additionally, China's…
Abstract
Purpose
Increasing academic communities and practitioners begin to explore a novel method to reduce environmental pollution and realize green logistics delivery. Additionally, China's Statistical Yearbook shows that the number of private cars has reached 165 million in China. Under this background, this study proposes a green delivery method by the combination of sharing vehicle (private cars) and IoT (Internet of things) from the perspective of vehicle energy efficiency and aims to improve the energy efficiency of social vehicles and provides more convenient delivery services.
Design/methodology/approach
This study builds an IoT architecture consisting of customer data layer, information collection layer, cloud optimization layer and delivery task execution layer. Especially in the IoT architecture, a clustering analysis method is used to determine the critical value of customers' classification and shared delivery, a routing optimization method is used to solve the initial solution in could layer and shared technology is used in the implementation of shared delivery.
Findings
The results show that the delivery method considering shared vehicles has a positive effect on improving the energy utilization of vehicles. But if all of delivery tasks are performed by the shared vehicle, the application effect may be counterproductive, such as delivery cost increases and energy efficiency decreases. This study provides a good reference for the implementation of green intelligent delivery business, which has a positive effect on the improvement of logistics operation efficiency.
Originality/value
This study designs a novel method to solve the green and shared delivery issues under the IoT environment, which integrates the IoT architecture. The proposed methodology is applied in a real case in China.
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Ming-Lang Tseng, Chih-Cheng Chen, Kuo-Jui Wu and Raymond Tan
This study integrates economic/ecology (eco)-attributes and performance to build a sustainable service supply chain management (SSCM) model.
Abstract
Purpose
This study integrates economic/ecology (eco)-attributes and performance to build a sustainable service supply chain management (SSCM) model.
Design/methodology/approach
This study proposes the use of the fuzzy Delphi method to screen for the less important attributes and applies a network data envelopment analysis to explore the hierarchical and eco-efficient network interrelationships. The causality and hierarchal eco-efficient model is acquired using a fuzzy decision-making trial and evaluation laboratory analysis
Findings
The findings are as follows: (1) the information and technology management process is derived by enhancing sustainable customer and supplier relationship management, and (2) the eco-efficient model is improved based on long-term relationships with suppliers – that is, synergistic suppliers improve the service chain quality and provide services in an appropriate and timely manner – and research and development coordination. The theoretical and managerial implications are discussed.
Research limitations/implications
The eco-efficient model reveals that the sustainable customer relationship management process, sustainable supplier relationship management process and information and technology management process are the major causal attributes in the model.
Practical implications
The eco-efficient model must be based on (1) long-term relationships with suppliers, (2) synergistic suppliers to improve service chain quality, (3) the provision of services in a timely manner and (4) research and development coordination.
Originality/value
Prior studies neglect to build an ecological economy model using the efficiency causality model of hierarchical interrelationships. Traditional SSCM fails to involve the triple bottom line performance toward sustainability.
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Ming-Lang Tseng, Shiou-Yun Jeng, Chun-Wei Lin and Ming K. Lim
Construction and demolition waste (CDW) continuously causes environmental and social problems. These formidable challenges lead to sustainable issues and are an increasingly…
Abstract
Purpose
Construction and demolition waste (CDW) continuously causes environmental and social problems. These formidable challenges lead to sustainable issues and are an increasingly urgent issue worldwide. Prior studies have neglected to link the triple bottom line (TBL) to a reliable estimation or empirical model for estimating CDW production performance and lack empirical sensitivity analysis in profit maximization. This study proposes an attribute analysis to build a cost–benefit analysis (CBA) to obtain profit maximization.
Design/methodology/approach
This study uses fuzzy set theory to develop a cost and benefit analysis (CBA) model to assess the sensitivity analysis in terms of its performance on addressing the environmental, economic and social aspects. The model is used to weigh the sum of benefits such as financial gain and total costs of actions taken to mitigate the negative impacts.
Findings
Based on the sensitivity analysis conducted, the environmental, economic and social mean scales were significantly changed, i.e. increased, and profits increased drastically. The results provide an insight into environmental legislation compliance, environmental investment and environmental impact as the cause attributes for the CDW recycling profit increase. The results prove that sensitivity analysis is viable to infer that a sustainable production performance can achieve more revenue and profit through an adequate selection of attributes regarding the TBL aspects to address the firm's uncertainty problem in multiple criteria analysis.
Originality/value
This study builds a CBA model to maximize profits for recycled CDW material by linking of environmental, economic and societal aspects for recycled CDW assessments. It considers a sustainability structure with criteria based on TBL aspects to assess production performance to realize the Sustainable Development Goals and presents fuzzy set theory and sensitivity analysis to solve the uncertainty problem in the construction industry.
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Maosheng Yang, Juan Li, Lei Feng, Shih-Chih Chen and Ming-Lang Tseng
This research proposes and examines a theoretical model grounded in anthropomorphism theory considering the curvilinear and linear relationships between service robot…
Abstract
Purpose
This research proposes and examines a theoretical model grounded in anthropomorphism theory considering the curvilinear and linear relationships between service robot anthropomorphism and consumer usage intention and explores the mediating effect of perceived risk.
Design/methodology/approach
To examine the developed model, two complementary studies are designed. In Study 1, multi-time data of 511 participants show that service robot anthropomorphism inverts U-shaped (curvilinear) relationship on consumer usage intention and perceived risk mediates this curvilinear relationship. In Study 2, multi-source data of 460 volunteers are used to confirm the findings of Study 1 and examine that consumer empathy moderates the complex nonlinear effect of service robot anthropomorphism on perceived risk, and the indirect curvilinear effect of service robot anthropomorphism on consumer usage intention through perceived risk.
Findings
This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.
Originality/value
This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.
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Mohammad Ali Beheshtinia, Mohammad Sajjad Safarzadeh, Masood Fathi, Morteza Ghobakhloo, Mostafa Al-Emran and Ming Lang Tseng
Healthcare wastes (HCWs) present substantial environmental and societal risks, including infection and exposure to hazardous substances. The aim of this study is to present a new…
Abstract
Purpose
Healthcare wastes (HCWs) present substantial environmental and societal risks, including infection and exposure to hazardous substances. The aim of this study is to present a new multi-criteria decision-making (MCDM) method, named the ELECTOR method, for selecting the best healthcare waste disposal method (HCWDM) based on a comprehensive list of criteria. The main research question of this study is: What is the prioritization of HCWDMs considering economic, environmental, technical and social criteria?
Design/methodology/approach
This research employs a novel hybrid MCDM method to evaluate and select suitable HCWDMs. Initially, a comprehensive set of criteria for assessing and prioritizing HCWDMs is established. Criteria weights are determined using the best-worst method. Subsequently, a hybrid MCDM method is introduced to rank the HCWDMs. Fuzzy numbers are applied to handle qualitative criteria uncertainties. The proposed method is applied to a real-world case study to prioritize HCWDMs.
Findings
A total of 24 criteria, including two novel criteria (“System process speed” and “System setup speed”), for evaluating and prioritizing the HCWDMs were identified from the literature review and case study analysis. The study showed that the key criteria influencing HCWDM selection were “Operation cost”, “Occupational hazards of human resources”, and “The impact of released substances on health”. Based on the results, the autoclave, encapsulation and hydroclave methods are identified as the most suitable HCWDMs for the studied case, respectively.
Originality/value
This study introduces a novel hybrid MCDM method tailored for HCWDM selection, enhancing the robustness of the decision-making. The inclusion of innovative criteria and the integration of fuzzy numbers to address qualitative ambiguities strengthen the originality of the findings. Specifically, introducing “System process speed” and “System setup speed” contributes to expanding the criteria landscape in HCWDM research.
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Anisha Banu Dawood Gani, Yudi Fernando, Shulin Lan, Ming K. Lim and Ming-Lang Tseng
This study aims to examine whether the cyber supply chain risk management (CSCRM) practices adopted by manufacturing firms contribute to achieving cyber supply chain (CSC…
Abstract
Purpose
This study aims to examine whether the cyber supply chain risk management (CSCRM) practices adopted by manufacturing firms contribute to achieving cyber supply chain (CSC) visibility. Studies have highlighted the necessity of having visibility across interconnected supply chains. Thus, this study examines the extent of CSCRM practices enabling CSC visibility to act as a mediator in achieving CSC performance.
Design/methodology/approach
A survey method was used to obtain data from the electrical and electronics manufacturing firms registered with the Federations of Malaysian Manufacturers directory. Data from 130 respondents were analysed using IBM SPSS and PLS-SEM.
Findings
This study empirically proves a dedicated governance team's integral role in setting the security tone within its CSC. The result also confirms the significant role that CSC visibility plays in achieving CSC performance. As theorised in the literature, there is also a strong direct relationship between CSC visibility and CSC performance, assuring manufacturing firms that investments and policies devised to improve CSC visibility are fruitful.
Originality/value
The significance of supply chain visibility in an integrated supply chain is recognised and studied using analytical models, behavioural techniques and case studies. Substantial empirical evidence on the CSCRM practices which contributes towards achieving supply chain visibility is still elusive. This study's major contribution lies in identifying CSCRM practices that can contribute towards achieving CSC visibility, and the mediating role CSC visibility plays in achieving CSC performance.